Overview

Dataset statistics

Number of variables13
Number of observations922733
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory91.5 MiB
Average record size in memory104.0 B

Variable types

DateTime1
Numeric12

Alerts

heart_rate is highly correlated with enhanced_speed and 1 other fieldsHigh correlation
enhanced_speed is highly correlated with heart_rate and 1 other fieldsHigh correlation
cadence is highly correlated with heart_rate and 1 other fieldsHigh correlation
slope_steep is highly correlated with slope_ascentHigh correlation
slope_ascent is highly correlated with slope_steepHigh correlation
slope_descent is highly correlated with slope_steepHigh correlation
enhanced_altitude is highly correlated with temp and 1 other fieldsHigh correlation
temp is highly correlated with enhanced_altitude and 2 other fieldsHigh correlation
wind_speed is highly correlated with enhanced_altitude and 2 other fieldsHigh correlation
wind_direct is highly correlated with temp and 1 other fieldsHigh correlation
timestamp has unique values Unique
rain has 527342 (57.2%) zeros Zeros
slope_steep has 537702 (58.3%) zeros Zeros
slope_ascent has 725108 (78.6%) zeros Zeros
slope_descent has 734893 (79.6%) zeros Zeros

Reproduction

Analysis started2023-01-17 19:51:42.563050
Analysis finished2023-01-17 19:52:54.229734
Duration1 minute and 11.67 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

timestamp
Date

UNIQUE

Distinct922733
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
Minimum2022-01-16 06:53:40
Maximum2023-01-16 15:25:45
2023-01-17T20:52:54.429260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:54.531706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

heart_rate
Real number (ℝ≥0)

HIGH CORRELATION

Distinct137
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143.4291577
Minimum58
Maximum196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2023-01-17T20:52:54.661668image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum58
5-th percentile119
Q1131
median139
Q3154
95-th percentile178
Maximum196
Range138
Interquartile range (IQR)23

Descriptive statistics

Standard deviation18.23099457
Coefficient of variation (CV)0.1271080083
Kurtosis-0.1126923096
Mean143.4291577
Median Absolute Deviation (MAD)10
Skewness0.4606228711
Sum132346817
Variance332.3691629
MonotonicityNot monotonic
2023-01-17T20:52:54.779765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13730648
 
3.3%
13429830
 
3.2%
13629605
 
3.2%
13928313
 
3.1%
13328178
 
3.1%
13828137
 
3.0%
13527762
 
3.0%
14027727
 
3.0%
13127418
 
3.0%
13226882
 
2.9%
Other values (127)638233
69.2%
ValueCountFrequency (%)
581
 
< 0.1%
602
 
< 0.1%
623
 
< 0.1%
635
< 0.1%
642
 
< 0.1%
655
< 0.1%
663
 
< 0.1%
676
< 0.1%
688
< 0.1%
696
< 0.1%
ValueCountFrequency (%)
1961
 
< 0.1%
1953
 
< 0.1%
19411
 
< 0.1%
19325
 
< 0.1%
19290
 
< 0.1%
191146
 
< 0.1%
190590
 
0.1%
1891048
0.1%
1881518
0.2%
1871249
0.1%

enhanced_speed
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4601
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.95800197
Minimum3.006
Maximum37.3536
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2023-01-17T20:52:54.922497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3.006
5-th percentile7.3224
Q110.4796
median11.4228
Q312.7296
95-th percentile17.9712
Maximum37.3536
Range34.3476
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation3.151807431
Coefficient of variation (CV)0.2635730818
Kurtosis1.618754781
Mean11.95800197
Median Absolute Deviation (MAD)1.0728
Skewness0.5641809857
Sum11034043.03
Variance9.933890082
MonotonicityNot monotonic
2023-01-17T20:52:55.043007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.5569304
 
1.0%
11.15289156
 
1.0%
11.25369136
 
1.0%
11.35448906
 
1.0%
11.45528885
 
1.0%
11.0528480
 
0.9%
11.65688376
 
0.9%
10.95127860
 
0.9%
11.2867842
 
0.8%
11.38687832
 
0.8%
Other values (4591)836956
90.7%
ValueCountFrequency (%)
3.0064
 
< 0.1%
3.01321
 
< 0.1%
3.024143
< 0.1%
3.02764
 
< 0.1%
3.03121
 
< 0.1%
3.03484
 
< 0.1%
3.0564113
< 0.1%
3.063
 
< 0.1%
3.06722
 
< 0.1%
3.07084
 
< 0.1%
ValueCountFrequency (%)
37.35361
 
< 0.1%
33.59161
 
< 0.1%
30.83761
 
< 0.1%
30.64
< 0.1%
30.23281
 
< 0.1%
30.19682
< 0.1%
30.16442
< 0.1%
30.1321
 
< 0.1%
30.03121
 
< 0.1%
29.72882
< 0.1%

distance
Real number (ℝ≥0)

Distinct706817
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7163.378849
Minimum0
Maximum26408.58
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2023-01-17T20:52:55.170835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile543.606
Q12891.13
median6304.52
Q310460.55
95-th percentile17046.194
Maximum26408.58
Range26408.58
Interquartile range (IQR)7569.42

Descriptive statistics

Standard deviation5161.984783
Coefficient of variation (CV)0.7206075362
Kurtosis-0.08478775201
Mean7163.378849
Median Absolute Deviation (MAD)3698.63
Skewness0.7228941544
Sum6609886056
Variance26646086.91
MonotonicityNot monotonic
2023-01-17T20:52:55.288421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3616.468
 
< 0.1%
8804.997
 
< 0.1%
62.697
 
< 0.1%
305.187
 
< 0.1%
797.427
 
< 0.1%
1926.267
 
< 0.1%
6706.097
 
< 0.1%
1472.77
 
< 0.1%
567.917
 
< 0.1%
2343.846
 
< 0.1%
Other values (706807)922663
> 99.9%
ValueCountFrequency (%)
02
< 0.1%
0.491
< 0.1%
0.511
< 0.1%
0.671
< 0.1%
0.871
< 0.1%
0.941
< 0.1%
1.082
< 0.1%
1.151
< 0.1%
1.191
< 0.1%
1.251
< 0.1%
ValueCountFrequency (%)
26408.581
< 0.1%
26402.761
< 0.1%
26389.241
< 0.1%
26371.511
< 0.1%
26363.731
< 0.1%
26361.951
< 0.1%
26360.592
< 0.1%
26353.621
< 0.1%
26348.241
< 0.1%
26347.241
< 0.1%

enhanced_altitude
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8278
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean315.5884289
Minimum129.4
Maximum2438.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2023-01-17T20:52:55.404509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum129.4
5-th percentile187.8
Q1197.4
median242.2
Q3380
95-th percentile617.8
Maximum2438.2
Range2308.8
Interquartile range (IQR)182.6

Descriptive statistics

Standard deviation212.1510517
Coefficient of variation (CV)0.6722396396
Kurtosis20.19028679
Mean315.5884289
Median Absolute Deviation (MAD)48.6
Skewness3.908724197
Sum291203857.8
Variance45008.06875
MonotonicityNot monotonic
2023-01-17T20:52:55.522786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
376.617401
 
1.9%
197.26639
 
0.7%
197.66506
 
0.7%
197.46169
 
0.7%
196.85824
 
0.6%
197.85698
 
0.6%
1985691
 
0.6%
1975595
 
0.6%
196.65552
 
0.6%
198.65419
 
0.6%
Other values (8268)852239
92.4%
ValueCountFrequency (%)
129.45
 
< 0.1%
129.61
 
< 0.1%
129.89
 
< 0.1%
13026
< 0.1%
130.220
 
< 0.1%
130.414
 
< 0.1%
130.614
 
< 0.1%
130.814
 
< 0.1%
13151
< 0.1%
131.226
< 0.1%
ValueCountFrequency (%)
2438.21
< 0.1%
24381
< 0.1%
2437.41
< 0.1%
2433.41
< 0.1%
2432.81
< 0.1%
24311
< 0.1%
2430.61
< 0.1%
2429.81
< 0.1%
2429.21
< 0.1%
2428.21
< 0.1%

cadence
Real number (ℝ≥0)

HIGH CORRELATION

Distinct97
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.79426335
Minimum31
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2023-01-17T20:52:55.642058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile81
Q185
median86
Q388
95-th percentile93
Maximum128
Range97
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.989776517
Coefficient of variation (CV)0.08147137401
Kurtosis15.48179407
Mean85.79426335
Median Absolute Deviation (MAD)2
Skewness-3.404833143
Sum79165198
Variance48.85697576
MonotonicityNot monotonic
2023-01-17T20:52:55.762976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86171552
18.6%
87148312
16.1%
85129400
14.0%
8482883
9.0%
8875997
8.2%
9149041
 
5.3%
9041090
 
4.5%
8339877
 
4.3%
8939126
 
4.2%
9229322
 
3.2%
Other values (87)116133
12.6%
ValueCountFrequency (%)
315
 
< 0.1%
3211
 
< 0.1%
3314
 
< 0.1%
3412
 
< 0.1%
3521
 
< 0.1%
3622
 
< 0.1%
3731
 
< 0.1%
3837
< 0.1%
3974
< 0.1%
4083
< 0.1%
ValueCountFrequency (%)
1282
 
< 0.1%
1261
 
< 0.1%
1255
 
< 0.1%
1244
 
< 0.1%
1232
 
< 0.1%
1225
 
< 0.1%
1216
 
< 0.1%
12020
< 0.1%
1199
< 0.1%
11822
< 0.1%

temp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct182
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.98952579
Minimum0
Maximum29.7
Zeros1258
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2023-01-17T20:52:55.890382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.5
Q16.8
median13.1
Q318.9
95-th percentile24.3
Maximum29.7
Range29.7
Interquartile range (IQR)12.1

Descriptive statistics

Standard deviation7.258684467
Coefficient of variation (CV)0.5588105821
Kurtosis-1.111004352
Mean12.98952579
Median Absolute Deviation (MAD)6.1
Skewness0.009899828831
Sum11985864.1
Variance52.68850019
MonotonicityNot monotonic
2023-01-17T20:52:56.128659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.222629
 
2.5%
3.820305
 
2.2%
17.218019
 
2.0%
10.316242
 
1.8%
18.216028
 
1.7%
3.615937
 
1.7%
5.215731
 
1.7%
9.615426
 
1.7%
18.515081
 
1.6%
1.314631
 
1.6%
Other values (172)752704
81.6%
ValueCountFrequency (%)
01258
 
0.1%
0.22936
 
0.3%
0.36366
0.7%
0.52805
 
0.3%
0.61374
 
0.1%
0.76056
0.7%
0.81772
 
0.2%
0.91508
 
0.2%
1.23734
 
0.4%
1.314631
1.6%
ValueCountFrequency (%)
29.71325
 
0.1%
27.45364
0.6%
27.25253
0.6%
26.74507
0.5%
26.55504
0.6%
26.23735
0.4%
25.23284
 
0.4%
24.78212
0.9%
24.64981
0.5%
24.37246
0.8%

wind_speed
Real number (ℝ≥0)

HIGH CORRELATION

Distinct113
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.393785093
Minimum0
Maximum28.1
Zeros6855
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2023-01-17T20:52:56.264300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.2
Q15.7
median7.2
Q310
95-th percentile16.1
Maximum28.1
Range28.1
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation4.03346618
Coefficient of variation (CV)0.480530075
Kurtosis3.870775129
Mean8.393785093
Median Absolute Deviation (MAD)2
Skewness1.651445675
Sum7745222.5
Variance16.26884943
MonotonicityNot monotonic
2023-01-17T20:52:56.391305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.537221
 
4.0%
9.733247
 
3.6%
528875
 
3.1%
5.827412
 
3.0%
6.823439
 
2.5%
4.822484
 
2.4%
921734
 
2.4%
5.221669
 
2.3%
6.221202
 
2.3%
6.617775
 
1.9%
Other values (103)667675
72.4%
ValueCountFrequency (%)
06855
0.7%
3.23244
 
0.4%
3.77041
0.8%
3.86102
0.7%
3.913450
1.5%
42584
 
0.3%
4.16312
0.7%
4.22196
 
0.2%
4.312505
1.4%
4.45870
0.6%
ValueCountFrequency (%)
28.11608
 
0.2%
27.81273
 
0.1%
26.31566
 
0.2%
25.61032
 
0.1%
25.51276
 
0.1%
24.43129
0.3%
23.33901
0.4%
21.81083
 
0.1%
21.11125
 
0.1%
19.75126
0.6%

wind_direct
Real number (ℝ≥0)

HIGH CORRELATION

Distinct197
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198.5998192
Minimum0
Maximum359
Zeros7686
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2023-01-17T20:52:56.534021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27
Q1112
median211
Q3278
95-th percentile343
Maximum359
Range359
Interquartile range (IQR)166

Descriptive statistics

Standard deviation99.8959487
Coefficient of variation (CV)0.5030012066
Kurtosis-0.9810755758
Mean198.5998192
Median Absolute Deviation (MAD)78
Skewness-0.3227512095
Sum183254607
Variance9979.200567
MonotonicityNot monotonic
2023-01-17T20:52:56.658546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8319854
 
2.2%
27816226
 
1.8%
29815015
 
1.6%
20014406
 
1.6%
24614302
 
1.5%
34313187
 
1.4%
15013106
 
1.4%
20112854
 
1.4%
26812696
 
1.4%
28211690
 
1.3%
Other values (187)779397
84.5%
ValueCountFrequency (%)
07686
0.8%
18178
0.9%
33244
 
0.4%
51204
 
0.1%
74981
0.5%
95223
0.6%
104719
0.5%
122268
 
0.2%
131282
 
0.1%
175242
0.6%
ValueCountFrequency (%)
3594147
0.4%
3586881
0.7%
3571571
 
0.2%
3554271
0.5%
3541644
 
0.2%
3531663
 
0.2%
3524404
0.5%
3511864
 
0.2%
3507775
0.8%
349809
 
0.1%

rain
Real number (ℝ≥0)

ZEROS

Distinct70
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.351917185
Minimum0
Maximum84.6
Zeros527342
Zeros (%)57.2%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2023-01-17T20:52:56.802126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.4
95-th percentile10.9
Maximum84.6
Range84.6
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation7.554861439
Coefficient of variation (CV)3.212214055
Kurtosis77.83837016
Mean2.351917185
Median Absolute Deviation (MAD)0
Skewness7.842994869
Sum2170191.6
Variance57.07593137
MonotonicityNot monotonic
2023-01-17T20:52:56.930957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0527342
57.2%
0.526574
 
2.9%
0.324959
 
2.7%
0.119688
 
2.1%
3.316692
 
1.8%
0.816221
 
1.8%
1.114007
 
1.5%
113893
 
1.5%
7.613376
 
1.4%
2.812951
 
1.4%
Other values (60)237030
25.7%
ValueCountFrequency (%)
0527342
57.2%
0.119688
 
2.1%
0.29914
 
1.1%
0.324959
 
2.7%
0.410206
 
1.1%
0.526574
 
2.9%
0.66553
 
0.7%
0.71311
 
0.1%
0.816221
 
1.8%
0.91537
 
0.2%
ValueCountFrequency (%)
84.65223
0.6%
24.23023
0.3%
23.31337
 
0.1%
22.64519
0.5%
21.96401
0.7%
19.35419
0.6%
18.93365
0.4%
18.53198
0.3%
181523
 
0.2%
16.11882
 
0.2%

slope_steep
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct60995
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.254801703
Minimum0
Maximum45
Zeros537702
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2023-01-17T20:52:57.064889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35.970149254
95-th percentile13.15789474
Maximum45
Range45
Interquartile range (IQR)5.970149254

Descriptive statistics

Standard deviation5.717967081
Coefficient of variation (CV)1.756778939
Kurtosis16.34449373
Mean3.254801703
Median Absolute Deviation (MAD)0
Skewness3.346409872
Sum3003312.94
Variance32.69514754
MonotonicityNot monotonic
2023-01-17T20:52:57.197065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0537702
58.3%
453084
 
0.3%
6.6666666671250
 
0.1%
6.1538461541230
 
0.1%
6.472491909949
 
0.1%
6.493506494943
 
0.1%
6.329113924935
 
0.1%
6.309148265910
 
0.1%
6.230529595807
 
0.1%
6.849315068790
 
0.1%
Other values (60985)374133
40.5%
ValueCountFrequency (%)
0537702
58.3%
0.41788549941
 
< 0.1%
0.48661800491
 
< 0.1%
0.60132291041
 
< 0.1%
0.60404711571
 
< 0.1%
0.6410256411
 
< 0.1%
0.67159167231
 
< 0.1%
0.67476383271
 
< 0.1%
0.67521944631
 
< 0.1%
0.7057163021
 
< 0.1%
ValueCountFrequency (%)
453084
0.3%
451
 
< 0.1%
44.989775051
 
< 0.1%
44.943820222
 
< 0.1%
44.943820222
 
< 0.1%
44.943820226
 
< 0.1%
44.943820221
 
< 0.1%
44.943820222
 
< 0.1%
44.943820221
 
< 0.1%
44.943820221
 
< 0.1%

slope_ascent
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct71
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06557129744
Minimum0
Maximum10
Zeros725108
Zeros (%)78.6%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2023-01-17T20:52:57.329789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.4
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.170820599
Coefficient of variation (CV)2.605112384
Kurtosis163.7083376
Mean0.06557129744
Median Absolute Deviation (MAD)0
Skewness6.906739497
Sum60504.8
Variance0.02917967705
MonotonicityNot monotonic
2023-01-17T20:52:57.459440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0725108
78.6%
0.288673
 
9.6%
0.251557
 
5.6%
0.421168
 
2.3%
0.48465
 
0.9%
0.67232
 
0.8%
0.23131
 
0.3%
12953
 
0.3%
0.82904
 
0.3%
0.62841
 
0.3%
Other values (61)8701
 
0.9%
ValueCountFrequency (%)
0725108
78.6%
0.23131
 
0.3%
0.251557
 
5.6%
0.288673
 
9.6%
0.2181
 
< 0.1%
0.484
 
< 0.1%
0.41914
 
0.2%
0.421168
 
2.3%
0.48465
 
0.9%
0.62841
 
0.3%
ValueCountFrequency (%)
107
< 0.1%
9.21
 
< 0.1%
8.81
 
< 0.1%
8.41
 
< 0.1%
81
 
< 0.1%
7.41
 
< 0.1%
6.82
 
< 0.1%
6.21
 
< 0.1%
5.61
 
< 0.1%
5.41
 
< 0.1%

slope_descent
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct71
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06401721842
Minimum0
Maximum10
Zeros734893
Zeros (%)79.6%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2023-01-17T20:52:57.591407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.4
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1710827664
Coefficient of variation (CV)2.672449236
Kurtosis83.39894901
Mean0.06401721842
Median Absolute Deviation (MAD)0
Skewness5.798203909
Sum59070.8
Variance0.02926931297
MonotonicityNot monotonic
2023-01-17T20:52:57.739938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0734893
79.6%
0.281790
 
8.9%
0.249297
 
5.3%
0.420802
 
2.3%
0.48395
 
0.9%
0.67163
 
0.8%
0.63176
 
0.3%
0.83019
 
0.3%
13015
 
0.3%
0.22210
 
0.2%
Other values (61)8973
 
1.0%
ValueCountFrequency (%)
0734893
79.6%
0.22210
 
0.2%
0.249297
 
5.3%
0.281790
 
8.9%
0.299
 
< 0.1%
0.466
 
< 0.1%
0.41779
 
0.2%
0.420802
 
2.3%
0.48395
 
0.9%
0.63176
 
0.3%
ValueCountFrequency (%)
101
< 0.1%
8.41
< 0.1%
7.81
< 0.1%
7.61
< 0.1%
6.81
< 0.1%
6.61
< 0.1%
6.41
< 0.1%
6.41
< 0.1%
6.21
< 0.1%
6.21
< 0.1%

Interactions

2023-01-17T20:52:48.776313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:10.166285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:13.127103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:15.978327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:19.692026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:22.943175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:26.742974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:30.638779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:33.854917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:37.695995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:41.247433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:45.051680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:49.101059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:10.501744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:13.372017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:16.297587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:19.931719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:23.250052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:27.050602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:30.917549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:34.095642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:37.991640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:41.615612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:45.385701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:49.413536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:10.744655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:13.614302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:16.638858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:20.164004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:23.551224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:27.385864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:31.189208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:34.382990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:38.280576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:41.912085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:45.709501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:49.716619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:10.982507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:13.849761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:16.950075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:20.423255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:23.847705image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:27.637523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:31.466255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:34.722547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:38.544385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:42.220189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:46.053779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:50.055280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:11.216447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:14.081197image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:17.321297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:20.647208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:24.173874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:27.907719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:31.743993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:35.051896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:38.818408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:42.517967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:46.339818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:50.332963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:11.463357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:14.319073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:17.654565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:20.893374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:24.490523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:28.171355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:31.997611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:35.370553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:39.103335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:42.800447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:46.715673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:50.591944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:11.716413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:14.542578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:17.984378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:21.142975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:24.807857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:28.432794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:32.256057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:35.734180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:39.522383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:43.159081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:46.961025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:50.849742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:11.950457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:14.775011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:18.290216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:21.370487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:25.134680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:28.730866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:32.506538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:36.093409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:39.784804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:43.469889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:47.275132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:51.111102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:12.183677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:15.008850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:18.592702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:21.703285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:25.456984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:29.121227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:32.960990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:36.379187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:40.058717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:43.793514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:47.607207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:51.360321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:12.402940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:15.237941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:18.898523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:22.020075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:25.736053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:29.471338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:33.170893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:36.698589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:40.360963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:44.100801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:47.897039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:51.622253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:12.629467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:15.475030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:19.194548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:22.322491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:26.070476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:29.830482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:33.394762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:37.031857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:40.645108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:44.392995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:48.174390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:51.863657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:12.864996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:15.697170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:19.443012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:22.639422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:26.435747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:30.227534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:33.623381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:37.380796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:40.912060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:44.708774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-17T20:52:48.469009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-01-17T20:52:57.974875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2023-01-17T20:52:58.127259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-17T20:52:58.293875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-17T20:52:58.457244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-17T20:52:58.629726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-01-17T20:52:52.096739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-17T20:52:52.890258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

timestampheart_rateenhanced_speeddistanceenhanced_altitudecadencetempwind_speedwind_directrainslope_steepslope_ascentslope_descent
02022-01-16 06:53:4080.09.104410.88378.482.00.311.2193.00.30.0000000.00.0
12022-01-16 06:53:4183.09.709213.57378.283.00.311.2193.00.37.4349440.00.2
22022-01-16 06:53:4289.09.874816.38378.083.00.311.2193.00.37.1174380.00.2
32022-01-16 06:53:4493.09.406821.85377.883.00.311.2193.00.33.6563070.00.2
42022-01-16 06:53:4896.09.370832.38377.682.00.311.2193.00.31.8993350.00.2
52022-01-16 06:53:5097.09.370837.23377.482.00.311.2193.00.34.1237110.00.2
62022-01-16 06:53:5197.09.338439.92377.482.00.311.2193.00.30.0000000.00.0
72022-01-16 06:53:5599.09.068446.59376.263.00.311.2193.00.317.9910040.01.2
82022-01-16 06:53:5699.08.600448.63375.877.00.311.2193.00.319.6078430.00.4
92022-01-16 06:53:58102.08.028053.57375.480.00.311.2193.00.38.0971660.00.4

Last rows

timestampheart_rateenhanced_speeddistanceenhanced_altitudecadencetempwind_speedwind_directrainslope_steepslope_ascentslope_descent
9227232023-01-16 15:25:12131.012.160814904.34269.488.03.615.3158.00.05.9171600.20.0
9227242023-01-16 15:25:13132.012.024014907.68269.888.03.615.3158.00.011.9760480.40.0
9227252023-01-16 15:25:17134.012.124814921.10270.688.03.615.3158.00.05.9612520.80.0
9227262023-01-16 15:25:19133.012.060014927.82271.087.03.615.3158.00.05.9523810.40.0
9227272023-01-16 15:25:23134.011.588414940.93271.887.03.615.3158.00.06.1022120.80.0
9227282023-01-16 15:25:27135.011.858414954.00272.287.03.615.3158.00.03.0604440.40.0
9227292023-01-16 15:25:32137.011.556014970.36272.887.03.615.3158.00.03.6674820.60.0
9227302023-01-16 15:25:33137.011.588414973.58272.887.03.615.3158.00.00.0000000.00.0
9227312023-01-16 15:25:39139.012.092414993.47273.887.03.615.3158.00.05.0276521.00.0
9227322023-01-16 15:25:45141.010.044015011.80274.679.03.615.3158.00.04.3644300.80.0